Traditional systems designed for task oriented dialog utilize knowledge present only in structured knowledge sources to generate responses. However, relevant information required to generate responses may also reside in unstructured sources, such as documents. Recent state of the art models such as HyKnow and SeKnow aimed at overcoming these challenges make limiting assumptions about the knowledge sources. For instance, these systems assume that certain types of information, such as a phone number, is always present in a structured knowledge base (KB) while information about aspects such as entrance ticket prices, would always be available in documents. In this paper, we create a modified version of the MutliWOZ-based dataset prepared by SeKnow to demonstrate how current methods have significant degradation in performance when strict assumptions about the source of information are removed. Then, in line with recent work exploiting pre-trained language models, we fine-tune a BART based model using prompts for the tasks of querying knowledge sources, as well as, for response generation, without making assumptions about the information present in each knowledge source. Through a series of experiments, we demonstrate that our model is robust to perturbations to knowledge modality (source of information), and that it can fuse information from structured as well as unstructured knowledge to generate responses.

翻译：传统面向任务型对话系统仅利用结构化知识源中的知识生成回复。然而，生成回复所需的相关信息也可能存在于非结构化知识源（如文档）中。近期如HyKnow和SeKnow等旨在克服这些挑战的最先进模型，对知识源做出了限制性假设。例如，这些系统假设电话号码等特定类型信息始终存在于结构化知识库中，而门票价格等方面信息则始终存在于文档中。本文基于SeKnow构建的MutliWOZ数据集创建了修改版本，以证明当取消关于信息源的严格假设时，现有方法性能会显著下降。随后，结合近期利用预训练语言模型的工作，我们采用提示机制对基于BART的模型进行微调，使其能够执行知识源查询与回复生成任务，而无需对每个知识源中的信息做出假设。通过系列实验，我们证明该模型对知识模态（信息源）扰动具有鲁棒性，并能融合结构化与非结构化知识生成回复。